verify_arithmetic

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Although no annotations are provided, the description discloses key behavioral traits: strict default matching to 2 decimal places and the optional 'tolerance' parameter for relative tolerance. It does not mention return format or error behavior, but the disclosed details are valuable and go beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear example, a bulleted list of claim types and required fields, and concise statements about default strictness and tolerance. Every sentence contributes necessary information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (9 parameters, 5 claim types), the description covers supported operations, required fields per type, and matching behavior. The only notable omission is the return value/output format, but the description is otherwise complete enough for an agent to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds significant value by specifying which fields are required for each claim_type and providing a concrete example. This clarifies parameter usage beyond the schema's field-level descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Verify that a claimed arithmetic result matches the actual computation.' It lists specific supported claim types (sum, weighted_average, percentage, growth, product), which distinguishes it from sibling tools like check_numeric_claims or validate_confidence that focus on broader validation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on how to structure inputs for each claim type, including a required JSON format and per-type field requirements. It does not explicitly contrast with sibling tools, but it offers clear context on when to use this tool (for verifying arithmetic claims) and how to construct calls.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.